Similar to [1]}, we also use Oracle to simulate human trainer feedback because it allows us to sweep parameters of feedback likelihood (\(L\) ), consistency level (\(C\) ) and the number of trainers. The Oracle was created by using Q-learning in the environment. After 10,000 episodes, we confirmed that the averaged reward converged, and it always clears the game by checking the total reward value, then store the Q-function table. The oracle uses the stored Q-function table with a greedy policy to generate the optimum action on a given state. Then the feedback is generated based on this optimum action in conjunction with feedback likelihood (\(L\) ) and consistency level (\(C\) .)
